Demand or Supply for Schooling in Rural India?

Transcription

1 Demand or Supply for Schooling in Rural India? Sripad Motiram Associate Professor Indira Gandhi Institute of Development Research, Mumbai, India. Ph: Lars Osberg University Research Professor, Dalhousie University, Halifax, Canada Preliminary, Please do not cite. Comments are welcome. * This paper would not have been possible without the very generous help of Dr. Indira Hirway, Director and Professor of Economics, Centre for Development Alternatives, Ahmedabad, India. Professor Hirway was instrumental in the design of the Indian Time Use Survey and her assistance in obtaining and interpreting the micro data from this survey is deeply appreciated.

2 Abstract This paper examines the education decisions of rural households in India, presents new evidence on informal instruction of children in the home and assesses the relative importance of household attributes and local educational quality for school attendance and human capital investment time. Micro-data from the Indian Time Use Survey (ITUS) conducted in Gujarat, Tamil Nadu, Madhya Pradesh, Meghalaya, Orissa and Haryana (covering 77,593 persons in 18,591 households) are matched to state level data from the 7th All India School Education Survey (AISES) on school quality and availability. Probit models of the determinants of school attendance and sample selection bias regression models of the total time invested in human capital acquisition by boys and girls (ages 6 to 1, 11 to 14 and 15 to 18) in rural India are estimated. The implications for school attendance and human capital investment time of scheduled caste status, parental education less than high school, household income less than median and school quality less than Tamil Nadu are simulated. Poor quality of local schools emerges as a crucially important negative influence on school attendance. 2

3 Demand or Supply for Schooling in Rural India? The crucial role of human capital makes it all the more essential to pay attention to the close relation between sensible public action and economic progress, since public policy has much to contribute to the expansion of education and the promotion of skill formation. The role of widespread basic education in those countries with successful growth-mediated progress cannot be overemphasized. J. Dreze and A.K. Sen India: Development and Participation (22:75) The value of education for development is increasingly recognized both in the instrumental sense of enabling rapid growth in GDP and in the direct attainment of human self-consciousness and capability. However, within India and particularly within rural India the distribution of educational opportunities and attainment is highly unequal. Schools in tents or outdoors or with absentee teachers coexist with schools whose teachers and resources are world class in quality and there is substantial variation across states within India in the average level, and the inequality, of quality in local schools. Although no individual family can decide the nature of their local school system, those systems are the product of a collective choice, which constrains individual choices. Even given the educational alternatives available to them in their local area, individual families may make very different decisions regarding their children s schooling choices which will have enormous implications for their children s lives. This paper therefore asks the question: whose choices matter more? How much of the inequality in human capital investment in rural India can be explained by variation in the availability and quality of local schooling, and how much can be attributed to variation in the attributes and choices of students and households? Section 1 begins with a brief description of our data sources the Indian Time Use Survey of and the 7th All India School Education Survey (AISES) and an overview of school quality, attendance and informal instruction in India. Section 2 then presents probit estimates of the probability of school attendance while Section 3 uses sample selection bias regression techniques to examine the determinants of total human capital investment time (i.e. time spent in school plus travelling to and from school plus 3

4 homework and in-home instructional time). Section 4 uses these estimates to compare the magnitude, and the inequality, of the human capital investment which is influenced by inequality in access to school facilities, relative to the impact of the social exclusion, low income or low education of Indian families. Section 5 concludes. 1.1 Data Description Between June, 1998 and July, 1999, the Central Statistical Organization of India conducted a pilot Time Use Survey (the ITUS). A stratified random sampling design, as followed in the National Sample Surveys (NSS), was used to survey 18,591 households (12,75 rural and 5,841 urban) with 77,593 persons, of whom 53,981 were rural and 23,612 were urban residents. The survey was conducted in four rounds during the year to capture seasonal variations in the time use patterns of the population. Two person teams of male and female interviewers stayed in each village or urban block for nine days to compile time diaries for normal, abnormal and weekly variant days. Respondent households were first visited to assess their weekly pattern of time use and then revisited to complete a full diary of activities concerning the previous day for all household members over six years of age. The data set contains an individual record of the day s activities of each adult and each child over the age of six and a household level record of household characteristics the common activities of household members can be identified by activity timing within the day and by the linkage of household identifiers. Although the sample design was explicitly constructed to capture differences in time use between normal and weekly variant or abnormal i days, in practice Hirway (2:24) noted that On an average, of the total 7 days, 6.51 were normal,.44 weekly variant day and.5 was abnormal day in rural areas people continue their normal activities on holidays also. This paper therefore focuses on time use on normal days. As Pandey (1999:1) noted: India has lot of socio-economic, demographic, geographic and cultural diversities. To ensure that all aspects of diversities are captured, Haryana, Madhya Pradesh, Gujarat, Orissa, Tamil Nadu and Meghalaya were chosen to represent northern, central, western, eastern, southern and north-eastern regions respectively. Although some might wonder whether six states data could fully capture the diversity of India, Hirway (2:11) has argued cross-checking of the results has confirmed that the sample is fairly representative of the country. In any event, this data 4

5 would be interesting even were this not the case, i.e. even if the data were only seen as a sample of the approximately 233 million people inhabiting these six states. Because the state and district of the respondent are recorded in the ITUS microdata, each respondent household can be exactly matched to state level data from the 7th All India School Education Survey (AISES), which collected comprehensive data on a census basis on every facet of school education in India, as of September 3, 22 ii - including the availability of schooling facilities in rural habitations, physical and educational facilities in schools, incentive schemes and beneficiaries, medium of instructions and languages taught, enrolment, teachers and their academic and professional qualifications, library, laboratory, ancillary staff and subject-wise enrolment at +2 stage of education. In addition, the enrolment and teachers in unrecognised schools, Alternative Schools and AIE Centers, Oriental Schools covering Sanskrit Pathshalas, Madarsas and Maktabs; Special Schools for children with disabilities, and Pre-primary Institutions are also covered. Unfortunately, in states other than Gujarat we could not identify the district of residence, so we have had to make do (for the moment) with state wide average measures of school facilities. In order to enable a more exact match between individual households and the characteristics of their local school system, we hope to be able to use district level data in future work. 1.2 The Supply Side Variation in School Availability and Quality Within India, there is remarkable variation across states in school availability and quality. As Table 1 indicates, the fraction of schools that have an average pupil/teacher ratio over 5 is 2.2% in the state of Kerala and.2% in Goa, but reaches 58% in Uttar Pradesh and 78.8% in Bihar. In the state of Manipur, 54.3% of schools have either no building at all or one constructed of material such as unburned bricks, bamboos, mud, grass, reeds, thatch or loosely backed stones. In Arunachal Pradesh, 3.9% of schools are thus constructed but in Goa it is only 1.2% and in Kerala even less (.6%). 5

6 Table 1 Indicators of School Quality in Indian States AISES 22 State or Union Territory %PTR >5* % No Building or Kaccha** Andhra Pradesh % % Arunachal Pradesh % 3.879% Assam % % Bihar % 8.843% Chhattisgarh 31.46% 7.123% Goa.193% 1.28% Gujarat 1.352% 9.894% Haryana %.376% Himachal Pradesh 3.819% 1.232% Jammu & Kashmir 6.77% % Jharkhand 52.74% 7.77% Karnataka 8.513% 3.642% Kerala 2.15%.647% Madhya Pradesh % 9.3% Maharashtra 9.716% 2.19% Manipur 1.11% % Meghalaya 6.923% 23.1% Mizoram 6.15% 32.49% Nagaland 1.849% % Orissa % 4.438% Punjab 2.622%.623% Rajasthan % 1.665% Sikkim.% % Tamil Nadu 12.5% 2.961% Tripura % % Uttar Pradesh % 1.945% Uttaranchal % 2.719% West Bengal 44.35% 9.55% * Percentage of primary schools (both rural and urban) where the Pupil to Teacher Ratio (PTR) is greater than 5. ** Percentage of rural primary schools without a building (tent or an open space) or with only a kaccha building. For any individual household, the characteristics of their local school system are an exogenous constraint iii. Parents must make choices, on behalf of their children, about the productivity of investing time in human capital acquisition, in expectation of greater future earnings by their children but where schools are unavailable or difficult to access, the option of continued school attendance may not be fully open. As well, where 6

7 schools are generally of low quality either in physical facilities or teacher/student ratio or in teaching resources reasonable parents may have systematically lower expectations of the productivity of spending time in school. As Hanushek et al (26:1) conclude: a student is much less likely to remain in school if attending a low quality school rather than a high quality school. For these reasons, we expect the probability of school attendance, and the total time invested in human capital acquisition to be, ceteris paribus, lower in localities with poorer, or less available, schools. However, as Handa (1999a:2) remarks: even if regional variations in schooling infrastructure can be related to household schooling choices, as several studies have shown, efficient policy decisions require knowledge of the particular dimensions of school infrastructure that matter most. 1.3 Investing Time Family Decisions about the Human Capital of Children Each day, families must allocate the scarce resource of household time to the competing alternatives of direct production of goods and services, market work to produce cash income, investment in future productive capacity and leisure. Because the importance of investment in the human capital of children has increasingly been recognized as a major determinant of economic development, and because inequality in access to such investment is central to the core ethical issue of equality of opportunity, time use data offers a unique window on both the efficiency and equity of the development process. In the ITUS, every individual s principal status (e.g. working in the household, working as a casual labourer, student, etc.) is given but because we also have direct information on whether an individual actually attends an educational institution, Table 2 can distinguish between school enrolment and actual school attendance. In both urban and rural areas, the fraction of children aged 6 to 18 who actually attended school on a normal day is about one fifth lower than the proportion identified as student even if the higher enrolment of urban areas (about 75%) implies a somewhat larger absolute differential (15 percentage points). As the top two rows of Table 2 illustrate, in both rural and urban areas roughly seventy percent of Indian children aged 6 to 1 attend school. In urban areas, the same proportion of both boys and girls remain in school for ages 11 to 14, and there is little gender differential in the drop to forty percent remaining in school when aged 15 to 18. In 7

8 the rural areas, however, gender differences in school attendance increase from five percentage points for 6 to 1 year olds to twelve percentage points among older age groups. In combination with a strong tendency for rural teens to leave school, this implies that by the age of 15 to 18 only about a fifth of rural girls are in school. The importance of intergenerational influences shows up clearly in Table 2. The 15 to 18 year old children of casual labourers in urban areas have a thirty five percentage point lower chance of school attendance, compared to wage workers. And the school attendance rate of rural girls aged 11 to 14 nearly doubles (increasing from 32% to 61%) if there is a literate adult female in the household. The ITUS records directly, for each child aged 6 or over, both time spent in informal learning in the home and in school attendance. To our knowledge, this paper offers the only available evidence in developing countries on the role which informal parental instruction may play in human capital acquisition. Historically, education outside school has sometimes been crucially important. In Scandinavia in the seventeenth century, for example, nearly universal literacy was achieved, as Johannson (1988: 137) notes, almost completely without the aid of a proper school system in the countryside. The responsibility for teaching children to read was ultimately placed on parents and godfathers. (A responsibility Swedish parents took seriously, given the possibility that Lutherans perceived of eternal damnation of the souls of the children who did not learn their catechism before confirmation, typically at age 13 or 14.) 8

10 The ITUS data also record the time each adult spent in Activity 521 TEACHING, TRAINING AND INSTRUCTION OF OWN CHILDREN, as Table 3 reports. About 6% of rural, and 18% of urban, households report this activity on a randomly selected normal day and when it occurs, families evidently take it seriously, with median time invested being a full hour. Since the time use diary methodology of the ITUS samples an individual day, we cannot distinguish the periodicity of episodes with this data (e.g. we cannot distinguish between the hypotheses that (a) 42% of rural households help with homework, but only for one day each week and (b) that 6% of rural households help with homework every day of the week.) Nevertheless, the difference between urban and rural families is apparent and strong within-family specialization is evident with an interesting gender reversal between rural areas (58% male) and urban areas (58% female). About 9% of the time, it is the head of household, or spouse thereof, who instructs children but in the remaining 1% of cases, it is married children within the household, or older siblings. And although it is clear from Table 2 that the literacy of adults strongly predicts school attendance, it is also clear that some illiterate parents do value their children s education strongly 14% of the rural adults who spend time instructing their children try to do so despite their own illiteracy. Because we can match the timing of informal adult educational activity with each child s record of whether they received instruction, we can tell which children within the household received informal adult attention, and who provided it. This paper focuses on the total time devoted to learning of each child, but because we can calculate both the aggregate amount and origin of informal instruction, we hope in future work to examine the determinants and the extent of any intra-family inequality in parental time invested in human capital. 1

11 Table 3 Time spent by households and individuals on: 521. TEACHING, TRAINING AND INSTRUCTION OF OWN CHILDREN % of Households which spend any time* Of Whom: 1 Adult is involved 2 Adults are involved >2 Adults are involved Of Whom: Scheduled Tribes Scheduled Castes Others Median time spent by households (mins)** % of adult individuals who spend any 521 time*** Of Whom: Men Women Non-Literate Literate Head of Household Spouse of Head of Household Married Child Spouse of Married Child Unmarried Child Others Median time spent by individuals (mins)**** Rural 5.45% 91.1% 8.64%.26% 8.14% 11.46% 8.4% % 57.55% 42.45% 14.5% 85.95% 47.63% 32.93% 8.42% 5.49% 2.36% 3.16% 6 Urban 17.14% 82.25% 17.32%.43% 1.88% 6.2% 91.91% 6 8.3% 41.8% 58.2% 6.9% 93.91% 4.28% 49.67% 2.% 5.4% 1.55% 1.45% 6 * Percentage of households in which at least one adult (older than 18 years) spends some time on 521..Percentages calculated over all households which have at least one child between 6 and 18 years of age. ** Calculated over positive values. *** Calculated over individuals who live in households which have at least one child **** Calculated over positive values. 11

12 In discussing the time which families invest in schooling, we would stress that we cannot assess in this paper the eventual productivity in higher future wages or other returns of the time invested in children s human capital iv. Our ITUS data only capture the quantity of time allocated to investment in education. School attendance is the largest single part of the total time devoted to learning of each respondent child but it is only part of the time investment which families make in children s human capital. Children also must do homework, and travel to school activities which the ITUS directly measures, in addition to time spent in class. Table 3A can therefore present a more complete picture, for each child, of the total investment of time than is available in other types of data although median class time is consistently about 5 ½ hours on a normal day, the median child aged 6 to1 spends about 7 ½ hours on schooling, which rises to about 9 hours for those who remain in school when aged 15 to 18, when one counts homework and travel time. Although the ITUS data contain no direct indicator of educational quality, many authors (e.g. Dreze and Sen, 22) have emphasized the very uneven nature of schooling in India. An indirect indicator of such inequality may be the substantial variation in homework time for example, among 15 to 18 year old boys in urban areas only about a third (33.9%) of all children (even fewer in rural areas) did any homework at all, but the median time of the 8% (= 33.9/42.4) of students aged 15 to 18 years old who did do homework was over 2 ½ hours! As well, when schools differ substantially in quality or availability, one can expect that student travelling time will be highly unequal, as some children will be able to attend the local school, while others must travel long distances in search of higher quality, or any available, schools. In the 15 to 18 age group, the median travel time (i.e. over positive travel times) was an hour a day. 12

13 Table 3A Time (minutes) spent on schooling by children (711,721 and 791)* Urban Ages 6-1 Ages Ages Boys Girls Boys Girls Boys Girls % % % % % % % class time (711) >** Median over positive class times Median over all homework (721) times % of all children homework > Median over positive homework times Median over all travel (791) times % of all children travel > Median over positive travel times Median Total ( ) Time 69.8% % % % % % % % % % % % % % % % % % 5 51 Rural % class time (711) > Median over positive class times Median over all homework (721) times % of all children homework > Median over positive homework times Median over all travel (791) times % of all children travel> Median over positive travel times Median Total ( ) Time 7.1% % % % % % % % % % % % % % % % % % 6 54 * If a child does not attend school (i.e. if 711=), his/her homework and travel times are set to zero. ** Calculated by dividing the number of children who have positive 711 time by the total number of children of that gender and in that age group (sample weights are used). All the percentages below are calculated in the same manner General Education: School/University/Other Educational Institutions Attendance 721. Studies, Homework And Course Review Related To General Education 791. Travel Related To Learning 13

14 2. The Probability of School Attendance Since the primary way in which children acquire human capital is by school attendance, it is crucial to understand the factors influencing the chances that they will, or will not, go to school. In the US, or in other affluent OECD nations, the occupational and educational background of parents has long been recognized as the crucial determinant of children s educational attainment and the intergenerational transmission of socioeconomic status. v However, the issue this paper seeks to address is the relative importance, in the context of rural India, of household level characteristics which influence the demand for education compared to the quality and availability of educational supply. Affluent OECD countries all have well-developed systems of public education which provide universally available access to schooling of reasonably high quality but India does not. Although there is much discussion of inequalities of educational opportunity in the school system within, for example, the USA, the disparities between US states in availability, physical facilities and teacher student ratios are far smaller than between Indian states. The monetary incentive to invest time in the education of children is the increase in their future earnings for present purposes we can summarize the expected future return in an individual s local labour market to the investment of an hour s current time, for average school quality, by some variable p i (where the subscript i refers to the ith individual student). If schools are far away, or of low quality, students have to spend more time to get the same amount of learning, so a parsimonious way to think about the problem of school quality and availability is to assume that an index q can summarize the productivity of the actual time which students invest in human capital acquisition. Low quality (or high travel time) schooling implies q < 1, while schooling of numeraire quality implies q = 1 and high quality schooling can be represented by q > 1. The return to an hour invested in Human Capital Acquisition is therefore dependent on both p i and q. Using the AISES data we can get some measures of quality (q*) at the state level, and if q** is the district level measured quality of schools and there is variation in school quality across districts within a state we can represent that district level variation by v, such that q* = E(q** + v). vi On any given day, a child of a particular age may not attend school either because they are not enrolled or because they are enrolled but absent 14

15 due to illness, other work obligations or the desire to skip school. It is reasonable to think that all these reasons for non-attendance are negatively related to both p i and q. We include q* as an explanatory variable, recognizing that within-state variability in local school quality will create attenuation bias, biasing downward the size and significance of any estimated coefficient. Parental characteristics matter for school attendance both because some families may have a greater taste for the non-monetary returns to education and because families differ in their ability to finance the costs of education (in particular, the foregone earnings or agricultural output of child labour) and in their discount rate on the future monetary returns to education we can summarize the family background characteristics which influence the demand of the ith child for schooling by a vector F i. In general, both supply and demand for school will matter for school attendance and if S i is the time a child spends in class, then: (1) Prob (S i > ) = f (p i, q*, F i ) Table 4 presents the results obtained when a Probit model of school attendance of rural boys and girls, ages 6 to 1, 11 to 14 and 15 to 18 is estimated using the ITUS data. In Table 4, AISES data is used to construct variables indicative of the availability and quality of the school system within each state specifically, the number of Primary or Primary and Upper Primary schools per-capita vii, the number of secondary schools percapita, the percentage of low quality primary schools (average pupil/teacher ratio over 5) and the percentage of schools with no building or a kuchcha viii facility. In each state, household micro-data is matched to the corresponding state wide indicators of the aggregate availability and quality of the local school system. A consistent finding in Table 4, with only a few exceptions, is the large and strongly statistically negative correlation between school attendance and our indicator of low quality school facilities and prevalence of high student/teacher ratios. For boys under 14, our availability measure (the number of primary and upper primary schools per capita) is not statistically significant but it is significant for girls. In Table 4, a [,1] dummy variable identifies households in which there is no literate adult female. The importance of female literacy for the school attendance of 15

16 children comes through very strongly for both boys and girls, for all age groups, this variable is very highly statistically significant and negatively correlated with school attendance. The educational background of the head of each household is measured by a series of dummy variables indicating the marginal influence of schooling attainment, relative to lower levels of school attainment. The base case is a household head with no formal education, so a [,1] dummy variable indicates whether an individual has some primary school, another [,1] dummy variable indicates whether an individual has finished primary school, and another [,1] dummy variable indicates whether an individual has finished middle school, etc. Anyone who has finished primary school will necessarily be coded [1] for both some primary and finished primary, while a middle school graduate will be coded [1] for each of some primary, finished primary and finished middle school so the cumulative influence of education is the sum of coefficients at earlier levels of education. It is evident that for both boys and girls aged 6 to 1, a crucial issue in attendance at primary school is whether or not one s parents have any education. ix Compared to the base case of no formal education, the dummy variable for some primary is a strongly significant determinant of school attendance for both boys and girls but the statistical insignificance of higher levels of school attainment indicates that there is no particular difference among parents with higher schooling levels in their desire for primary school attendance by their children. However, for children aged 11 to 14, the crucial level of parental education shifts up to primary school i.e. parents with more than primary school are all alike in wanting at least a middle school education for their children. Similarly, the probability of school attendance for boys aged 15 to 18 increases with father s education over the range up to middle school. Broadly speaking, we can interpret these findings as indicative of an escalating intergenerational norm within families for more education. The base category for household head occupational status is labourer and [,1] dummy variables indicate whether the head is self-employed or other only the other category is statistically significant. Current household income is approximated in the ITUS by aggregate monthly expenditure per capita. Since the respondents to the ITUS were asked a single summary 16

17 question about total average monthly expenditures by the household (rather than the series of questions on categories of consumption which a household expenditure survey would use to add up total consumption) we are cautious x about possible measurement error in this variable particularly since it is unlikely to include self-production of food and fuel. Nevertheless, if income is uncorrelated with the school attendance of boys aged 6 to 1 and 1 to 14 (columns 1 and 3), while the positive and statistically significant coefficients in column 2 and 4 indicate that family income matters for similarly aged girls, it is some evidence of gender bias in early schooling. More generally over and above the direct influence of parental education the strongly statistically significant positive correlation of household income and school attendance for both boys and girls ages 15 to 18 is an important indicator of inequality of opportunity. Columns 1 and 2 indicate that the social disadvantage of membership in a Scheduled Caste or Tribe xi is directly correlated with lower early school attendance, in addition to the influence of household income or parental education, but columns 3 to 6 show no statistically significant correlation with later attendance. Although we include a dummy variable for Female Household Head status and another for landlessness, neither are statistically significant, once we have controlled for income and education. 17

19 Ages 6-1 Ages Ages Boys Girls Boys Girls Boys Girls Sample size Log likelihood Time Invested in Education The total time invested in education by each child (HK i ) is the sum of the time they spend in class (S i ) plus the time spent doing homework (H i ) plus travel time (T i ), to and from school as equation (2) summarizes. (2) HK i = S i + H i + T i Generally speaking, it is not possible to attend school for ½ or ¾ hours each day the normal school day is a lump of time. On any given day, some of the children who would normally be in school will be absent, due to illness, or competing work responsibilities, or because they want to skip school. We only observe S i for those children who actually attend school on the day surveyed by ITUS, so the estimation of expected HK i is a classic sample selection bias problem in the sense of Heckman (1979). Hence, we denote as λ i the Inverse Mills Ratio derived from the probit estimation of equation (1) above and denote as X i the variables influencing time allocation to schooling and to other time uses within the household. A general form of the estimating equation can then be summarized as in (3): (2) E (HK i ) =f (p i, q*, F i, X i,, λ i ) In other work (Motiram and Osberg, 27) we have examined the 18.6% of households in rural India who have to spend time collecting water for daily use. For the development process, an important implication of carrying water is its possible impact on human capital acquisition specifically, on the time that children will spend in school, travelling or doing homework. Rural women who spend an average of 47 minutes a day carrying water do not have that time available to spend attending to their children unless perhaps they can delegate the task of fetching water to their teenage daughters, 19

20 which may be part of the reason their daughters withdraw from school. Even if children are not asked to carry water themselves, the fact that someone (usually the mother) has to spend time on this task means that children may be asked to perform other household chores which implies that total household time spent in water collection may affect school attendance and human capital investment. Given that Table 4 shows the importance of adult female education for the school attendance of their children, this impact of water collection time on female investment in education can be expected to have implications over many future generations. Table 5 reports Heckit estimates (i.e. Ordinary Least Squares estimates with the Inverse Mills Ratio calculated from the regressions reported in Table 4) of equation (2) for boys and girls for three age groups (6-1, and 15-18). A consistent implication of Table 5 is that public policy matters for human capital investment time. In all age groups, and for both genders, the amount of time a household has to spend collecting water for daily use is negatively correlated with the amount of time spent on the education of children. Public policy on water delivery therefore matters directly for the well-being of the women who would otherwise have to perform the daily drudgery of carrying water xii, and indirectly for the future earnings and well-being of the children whose investment in education is lessened. Public policy on the availability and quality of schooling also has a clear impact. For both boys and girls, aged 6 to 1 and 11 to 14, the quality of school buildings is strongly significant and negatively associated with the human capital investment time of children. With the exception of girls 11 to 14, the local prevalence of large classes (PTR > 5) is similarly negatively correlated with time spent on education. Unfortunately, we do not as yet have quality measures for secondary schools that are comparable to those available for primary schools, so these variables do not appear for the age group regressions but for younger age groups, Table 5 is consistent with the hypothesis that families invest more of their children s time in education, in places where the quality of the local schools is better. Another lesson of Table 5 is the non-homogeneity of impacts by level of education. Whether a child comes from a scheduled caste or scheduled tribe family is not statistically significant for time spent on early education (ages 6 to 1), but is statistically significant and negatively associated with time spent in later years (11 to 14 and 15 to 18) for both boys and girls. 2

21 In the labour supply literature, a distinction is often drawn between the extensive margin of labour supply (when people who were not previously working get a job) and the intensive margin (when people who are already working decide to supply more or fewer work hours). The same terminology is useful here. Reading Tables 4 and 5 together, we have strong indications from Table 4 that the presence of literate females in the household is important for the extensive margin (i.e. for school attendance), but Table 5 indicates that, conditional on school attendance, this variable is not (except for girls 15 to 18) important at the intensive margin (i.e. in determining the amount of time spent by students on their schooling) xiii. Income (more exactly, Monthly per-capita expenditure) does not have a statistically significant association with either the probability of attendance or hours of time input for 6 to 1 year olds or 11 to 14 year old boys. For older children, and for girls 11 to 14, a positive and statistically significant coefficient at the intensive margin of attendance contrasts with a generally insignificant coefficient on hours studied, conditional on attendance. Similarly, the education of the head of household seems to matter more at the extensive margin of attendance than at the intensive margin of hours studied. Most (i.e. 7%) children do attend school when aged 6 to 1, and there is no evidence for sample selection bias at those ages (i.e. the Inverse Mills Ratio is not significant) but attendance falls for 11 to 14 year olds, when there is evidence for sample selection bias. 21

23 4. Quantitative Implications In rural India in 1999, over thirty percent of boys aged 11 to 14, and over forty percent of girls, did not attend school. Why do so many families in rural India not invest in the human capital of their children? How much is due to the barriers of caste? How much does the poor education of parents, which might produce ignorance of the benefits of education, actually matter? Could it be that low family income, and a consequent need for immediate earnings by children, is the largest explanatory factor? Or is the most quantitatively important explanation to be found in the low quality of education which is available? Table 6 presents the quantitative implications, if the econometric estimates of the determinants of school attendance reported in Table 4 and the hours of investment estimates of Table 5 can be interpreted as causal. In Table 6, the four thought experiments simulated are: (1) remove the influence of scheduled caste or tribe membership; (2) assume that all heads of household have at least a high school education and all families have some literate female adults; (3) assume that all families have incomes of Rs. 4 xiv or more (i.e. all families with less income than the median for rural households are brought up to that level); (4) increase the quality of local schooling to the level observed in Tamil Nadu in 22, in those states which fall below Tamil Nadu. In Table 6, the top panel reports actual outcomes (as measured in ITUS data). Each simulation case listed below that reports the difference between no change and simulated outcomes assuming the specified change. We report simulation results only for variables whose coefficient was significantly (at 5%) different from zero in Tables 4 and 5. In each simulation, some individuals attributes do not change e.g. we assume that simulation (1) only affects the children coming from Scheduled caste or tribe families, and that simulation (2) only affects the children coming from households with less than high school education of the head or female illiteracy. 23

24 For affected cases, each simulation proceeds in two steps. Using the results of Table 4, we first predict the expected value of the probability of school attendance of each individual whose attributes are assumed to change, with and without the change (e.g. in Simulation 1, with and without the influence of Scheduled Caste membership). To that expected value, we add a random draw from the error term implied in Table 4. We compare, for each individual, the calculated probability of school attendance with a random number drawn from a uniform distribution in order to assign that observation to school attendance, or not. The change in school attendance reported is the difference between a simulation which turns off, and another simulation turning on, the influence of the specific variable of interest (e.g. in Simulation I, the influence of SC/ST status). For the population of affected individuals who are now simulated to attend school, the second step calculates the expected value of human capital investment time given their simulated attributes (including the influence of sample selection bias, as calculated using the simulated value of the individual s Inverse Mills Ratio) and adds a random error from the unexplained variance implied in Table 5 results. We then add together the actual outcomes of those individuals who were unaffected by the simulation and the simulated outcomes of the affected population and we compare that total with the simulated totals assuming no change in population characteristics. We simulate the quantitative implications of our estimates in this way because we want to know the aggregate implications, across the distribution of actual characteristics of all people, of our econometric results assuming the relationship is causal. In each thought experiment, human capital investment can be expected to change at both the extensive margin (school attendance) and intensive margin (time input of students). Given the non-linear nature of probit estimates, changes can be expected to be dependent in a fairly complex way on the distribution of characteristics in the population. By design, we compare the implications of three quite dramatic thought experiments about households (i.e. nobody has less than high school, incomes are all brought up to the median and caste distinctions are suddenly non-existent) with a more plausible possibility that all other states are at least as good as Tamil Nadu in school quality. Tamil Nadu was chosen as comparator because it is a large state in our sample with good but not the best school quality. It therefore provides a within sample basis for estimates, and 24

25 represents an entirely plausible level of possible achievement of school quality for other states (see Table 1). Table 6 Simulations of the Impact of SC/ST, Parental Education, Income and Poor Quality Schools* Ages 6-1 Ages Ages Boys Girls Boys Girls Boys Girls % Attendance % Attendance for SC/ST Median Human Capital Time* Median Human Capital Time for SC/ST Simulation I (SC/ST) Difference in % Attendance Difference in % Attendance for SC/ST Difference in Median Human Capital Time Difference in Median Human Capital Time for SC/ST Simulation II (Parental Education) Difference in % Attendance Difference in Median Human Capital Time Simulation III (Income) Difference in % Attendance Difference in Median Human Capital Time Simulation IV (Quality) Difference in % Attendance Difference in Median Human Capital Time *We report only simulations involving statistically significant (at 5%) coefficients. ** All Medians calculated over positive values In presenting Table 6, we are aware that we are comparing a plausible policy scenario (Tamil Nadu school quality) about changes to the supply of schooling with several arguably less plausible scenarios (e.g. no rural household having income less than the 1999 median) which might affect the demand by households for education. We do this, despite our belief that attenuation bias due to measurement error will mean we have probably underestimated the true association between school quality and schooling choices, because we want to emphasize our basic conclusion that the influence of the 25

26 supply of poor school quality on the school attendance decisions of rural families in India dominates the influence of personal characteristics like scheduled caste membership or low household income. Because most people are not members of Scheduled Castes or Scheduled Tribes, and most people are therefore not themselves affected by the marginalization of SC/ST members, there is not a large aggregate impact, for the population as a whole, when the stigma of membership in these groups is removed e.g. for 6 to 1 year olds, an increase of 2.32 percentage points in the school attendance of boys, and 2.6 percentage points for girls. However, one should not think of the SC/ST issue just in terms of aggregate human capital formation and aggregate growth. If, for the same age group, one considers only members of scheduled castes and tribes, the change in attendance rates and median human capital investment time is clearly larger: 6.2 percentage points and +9.3 minutes for boys ( percentage points and minutes for girls). Nevertheless, given the continuing political controversies surrounding the administrative mechanisms (such as reserved places) used to encourage the educational attainment of Scheduled Castes/Tribe and other disadvantaged children, it is perhaps interesting to note that the schooling of SC/ST children would also benefit from general improvements in school quality which might be a policy choice with more widespread appeal. If there were no special treatment of SC/ST members, but the local school quality was improved to Tamil Nadu standards, the increase in school attendance of 6 to 1 year old SC/ST boys is simulated to be 4.1 percentage points (for girls 5.8 percentage points) which would be a substantial fraction of the improvement to be expected from policy targeted on SC/ST members alone. The results of our Simulation III which increases the income of all belowmedian households to approximately the median monthly rural expenditure level can be summarized as: little, if any, impact for a very large thought experiment. The small size (where statistically significant) of the coefficient on income in Table 4 and 5 drives a strong conclusion that inequality in schooling and human capital may play an important role in generating inequality in income, but not the reverse. The major message of Table 6 is two-fold: [a] the importance of public policy in the supply of school quality for current educational choices and [b] the lagged impact of 26

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